Overview

Dataset statistics

Number of variables19
Number of observations169909
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 MiB
Average record size in memory152.0 B

Variable types

NUM13
CAT4
BOOL2

Warnings

artists has a high cardinality: 33375 distinct values High cardinality
name has a high cardinality: 132940 distinct values High cardinality
release_date has a high cardinality: 10882 distinct values High cardinality
name is uniformly distributed Uniform
id has unique values Unique
instrumentalness has 46087 (27.1%) zeros Zeros
key has 21499 (12.7%) zeros Zeros
popularity has 27357 (16.1%) zeros Zeros

Reproduction

Analysis started2020-12-04 09:58:49.448606
Analysis finished2020-12-04 10:00:05.627417
Duration1 minute and 16.18 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

acousticness
Real number (ℝ≥0)

Distinct4714
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4932139761
Minimum0
Maximum0.996
Zeros21
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-12-04T11:00:05.807417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00144
Q10.0945
median0.492
Q30.888
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.7935

Descriptive statistics

Standard deviation0.3766270623
Coefficient of variation (CV)0.7636179844
Kurtosis-1.613842362
Mean0.4932139761
Median Absolute Deviation (MAD)0.397
Skewness0.008720481123
Sum83801.49347
Variance0.1418479441
MonotocityNot monotonic
2020-12-04T11:00:06.094414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.99530361.8%
 
0.99422901.3%
 
0.99317411.0%
 
0.99214980.9%
 
0.99112610.7%
 
0.9911710.7%
 
0.99610480.6%
 
0.98910310.6%
 
0.9889130.5%
 
0.9878110.5%
 
Other values (4704)15510991.3%
 
ValueCountFrequency (%) 
021< 0.1%
 
1e-061< 0.1%
 
1.01e-063< 0.1%
 
1.03e-061< 0.1%
 
1.05e-062< 0.1%
 
ValueCountFrequency (%) 
0.99610480.6%
 
0.99530361.8%
 
0.99422901.3%
 
0.99317411.0%
 
0.99214980.9%
 

artists
Categorical

HIGH CARDINALITY

Distinct33375
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
['Эрнест Хемингуэй']
 
1215
['Francisco Canaro']
 
938
['Эрих Мария Ремарк']
 
781
['Ignacio Corsini']
 
620
['Frank Sinatra']
 
592
Other values (33370)
165763 
ValueCountFrequency (%) 
['Эрнест Хемингуэй']12150.7%
 
['Francisco Canaro']9380.6%
 
['Эрих Мария Ремарк']7810.5%
 
['Ignacio Corsini']6200.4%
 
['Frank Sinatra']5920.3%
 
['Bob Dylan']5390.3%
 
['The Rolling Stones']5120.3%
 
['Johnny Cash']5020.3%
 
['The Beach Boys']4910.3%
 
['Elvis Presley']4880.3%
 
Other values (33365)16323196.1%
 
2020-12-04T11:00:06.516417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19572 ?
Unique (%)11.5%
2020-12-04T11:00:06.826417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length661
Median length17
Mean length23.32801676
Min length5

danceability
Real number (ℝ≥0)

Distinct1232
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5381497172
Minimum0
Maximum0.988
Zeros147
Zeros (%)0.1%
Memory size1.3 MiB
2020-12-04T11:00:07.196513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.233
Q10.417
median0.548
Q30.667
95-th percentile0.813
Maximum0.988
Range0.988
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.175345782
Coefficient of variation (CV)0.3258308542
Kurtosis-0.4251858742
Mean0.5381497172
Median Absolute Deviation (MAD)0.125
Skewness-0.2128955812
Sum91436.4803
Variance0.03074614328
MonotocityNot monotonic
2020-12-04T11:00:08.016517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.5654360.3%
 
0.5784130.2%
 
0.6124100.2%
 
0.5594010.2%
 
0.5563990.2%
 
0.6023970.2%
 
0.613970.2%
 
0.6223970.2%
 
0.5453930.2%
 
0.5463910.2%
 
Other values (1222)16587597.6%
 
ValueCountFrequency (%) 
01470.1%
 
0.05511< 0.1%
 
0.05592< 0.1%
 
0.05621< 0.1%
 
0.05692< 0.1%
 
ValueCountFrequency (%) 
0.9881< 0.1%
 
0.9862< 0.1%
 
0.9851< 0.1%
 
0.9821< 0.1%
 
0.983< 0.1%
 

duration_ms
Real number (ℝ≥0)

Distinct50212
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231406.159
Minimum5108
Maximum5403500
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-12-04T11:00:08.278490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5108
5-th percentile111987
Q1171040
median208600
Q3262960
95-th percentile410181.4
Maximum5403500
Range5398392
Interquartile range (IQR)91920

Descriptive statistics

Standard deviation121321.9232
Coefficient of variation (CV)0.5242813059
Kurtosis116.0062337
Mean231406.159
Median Absolute Deviation (MAD)44173
Skewness6.48952671
Sum3.931798906e+10
Variance1.471900905e+10
MonotocityNot monotonic
2020-12-04T11:00:08.533517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19200055< 0.1%
 
18000053< 0.1%
 
24000050< 0.1%
 
18600050< 0.1%
 
18400049< 0.1%
 
16000047< 0.1%
 
16800046< 0.1%
 
16900045< 0.1%
 
17000044< 0.1%
 
17500044< 0.1%
 
Other values (50202)16942699.7%
 
ValueCountFrequency (%) 
51081< 0.1%
 
59911< 0.1%
 
63621< 0.1%
 
64671< 0.1%
 
88532< 0.1%
 
ValueCountFrequency (%) 
54035001< 0.1%
 
42700341< 0.1%
 
42694071< 0.1%
 
41202582< 0.1%
 
38163731< 0.1%
 

energy
Real number (ℝ≥0)

Distinct2332
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4885931304
Minimum0
Maximum1
Zeros10
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-12-04T11:00:08.765489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0765
Q10.263
median0.481
Q30.71
95-th percentile0.925
Maximum1
Range1
Interquartile range (IQR)0.447

Descriptive statistics

Standard deviation0.267389933
Coefficient of variation (CV)0.5472650276
Kurtosis-1.097612674
Mean0.4885931304
Median Absolute Deviation (MAD)0.223
Skewness0.07736834862
Sum83016.37019
Variance0.07149737625
MonotocityNot monotonic
2020-12-04T11:00:09.009490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.282360.1%
 
0.4592340.1%
 
0.2542340.1%
 
0.3412330.1%
 
0.2992330.1%
 
0.3062320.1%
 
0.2452300.1%
 
0.312300.1%
 
0.322300.1%
 
0.2672280.1%
 
Other values (2322)16758998.6%
 
ValueCountFrequency (%) 
010< 0.1%
 
1.99e-052< 0.1%
 
2.01e-056< 0.1%
 
2.02e-055< 0.1%
 
2.03e-0514< 0.1%
 
ValueCountFrequency (%) 
120< 0.1%
 
0.99925< 0.1%
 
0.99838< 0.1%
 
0.99756< 0.1%
 
0.99667< 0.1%
 

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
155490 
1
 
14419
ValueCountFrequency (%) 
015549091.5%
 
1144198.5%
 
2020-12-04T11:00:09.180486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

id
Categorical

UNIQUE

Distinct169909
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
47BBI51FKFwOMlIiX6m8ya
 
1
5T4Wez0kAQRMnzdHIMQDKI
 
1
0UmIdWRGDL5gkXnqG9ZozM
 
1
35LDid9nvuYrUyZk5qGD0i
 
1
5CXIWgBcyg4qpHIrcOF1da
 
1
Other values (169904)
169904 
ValueCountFrequency (%) 
47BBI51FKFwOMlIiX6m8ya1< 0.1%
 
5T4Wez0kAQRMnzdHIMQDKI1< 0.1%
 
0UmIdWRGDL5gkXnqG9ZozM1< 0.1%
 
35LDid9nvuYrUyZk5qGD0i1< 0.1%
 
5CXIWgBcyg4qpHIrcOF1da1< 0.1%
 
0vdnSGPeqDln5UHIcYYfS31< 0.1%
 
2jFF9mHIt17vWbndKFWwch1< 0.1%
 
5M9TH1OD92fWsWqp5we4mY1< 0.1%
 
7z4lC3454haqc76rlV2uCg1< 0.1%
 
69XUpOpjzDKcfdxqZebGiI1< 0.1%
 
Other values (169899)169899> 99.9%
 
2020-12-04T11:00:10.063489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique169909 ?
Unique (%)100.0%
2020-12-04T11:00:10.338489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5401
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1619371431
Minimum0
Maximum1
Zeros46087
Zeros (%)27.1%
Memory size1.3 MiB
2020-12-04T11:00:10.585494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000204
Q30.0868
95-th percentile0.903
Maximum1
Range1
Interquartile range (IQR)0.0868

Descriptive statistics

Standard deviation0.309328882
Coefficient of variation (CV)1.910178703
Kurtosis1.119102571
Mean0.1619371431
Median Absolute Deviation (MAD)0.000204
Skewness1.681508995
Sum27514.57805
Variance0.09568435726
MonotocityNot monotonic
2020-12-04T11:00:10.822490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04608727.1%
 
0.9171960.1%
 
0.9161930.1%
 
0.9131890.1%
 
0.9011820.1%
 
0.9041790.1%
 
0.9141780.1%
 
0.9111770.1%
 
0.8941770.1%
 
0.9221760.1%
 
Other values (5391)12217571.9%
 
ValueCountFrequency (%) 
04608727.1%
 
1e-0628< 0.1%
 
1.01e-0667< 0.1%
 
1.02e-06890.1%
 
1.03e-0670< 0.1%
 
ValueCountFrequency (%) 
110< 0.1%
 
0.99913< 0.1%
 
0.99810< 0.1%
 
0.9973< 0.1%
 
0.9966< 0.1%
 

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.200519101
Minimum0
Maximum11
Zeros21499
Zeros (%)12.7%
Memory size1.3 MiB
2020-12-04T11:00:11.015490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.515256798
Coefficient of variation (CV)0.6759434452
Kurtosis-1.272498752
Mean5.200519101
Median Absolute Deviation (MAD)3
Skewness0.004956029235
Sum883615
Variance12.35703036
MonotocityNot monotonic
2020-12-04T11:00:11.209485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
02149912.7%
 
72075712.2%
 
21882111.1%
 
91762810.4%
 
5163369.6%
 
4129217.6%
 
1128167.5%
 
10120567.1%
 
8107116.3%
 
11105936.2%
 
Other values (2)157719.3%
 
ValueCountFrequency (%) 
02149912.7%
 
1128167.5%
 
21882111.1%
 
371854.2%
 
4129217.6%
 
ValueCountFrequency (%) 
11105936.2%
 
10120567.1%
 
91762810.4%
 
8107116.3%
 
72075712.2%
 

liveness
Real number (ℝ≥0)

Distinct1741
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2066903494
Minimum0
Maximum1
Zeros13
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-12-04T11:00:11.417488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0595
Q10.0984
median0.135
Q30.263
95-th percentile0.617
Maximum1
Range1
Interquartile range (IQR)0.1646

Descriptive statistics

Standard deviation0.1767964904
Coefficient of variation (CV)0.8553688691
Kurtosis4.91567912
Mean0.2066903494
Median Absolute Deviation (MAD)0.053
Skewness2.145796305
Sum35118.55057
Variance0.03125699902
MonotocityNot monotonic
2020-12-04T11:00:11.640290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.11118431.1%
 
0.10916391.0%
 
0.1116281.0%
 
0.10816201.0%
 
0.10715270.9%
 
0.10614960.9%
 
0.11214720.9%
 
0.10514670.9%
 
0.10313800.8%
 
0.10413770.8%
 
Other values (1731)15446090.9%
 
ValueCountFrequency (%) 
013< 0.1%
 
0.009671< 0.1%
 
0.01011< 0.1%
 
0.01031< 0.1%
 
0.01161< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.9991< 0.1%
 
0.9982< 0.1%
 
0.9975< 0.1%
 
0.9963< 0.1%
 

loudness
Real number (ℝ)

Distinct25313
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.3702893
Minimum-60
Maximum3.855
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-12-04T11:00:11.869382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-21.889
Q1-14.47
median-10.474
Q3-7.118
95-th percentile-4.096
Maximum3.855
Range63.855
Interquartile range (IQR)7.352

Descriptive statistics

Standard deviation5.66676463
Coefficient of variation (CV)-0.4983835045
Kurtosis1.911366406
Mean-11.3702893
Median Absolute Deviation (MAD)3.603
Skewness-1.071208446
Sum-1931914.485
Variance32.11222137
MonotocityNot monotonic
2020-12-04T11:00:12.062175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-7.43627< 0.1%
 
-7.00627< 0.1%
 
-7.63226< 0.1%
 
-9.29826< 0.1%
 
-8.3226< 0.1%
 
-6.94226< 0.1%
 
-11.81525< 0.1%
 
-8.78925< 0.1%
 
-7.56625< 0.1%
 
-11.45125< 0.1%
 
Other values (25303)16965199.8%
 
ValueCountFrequency (%) 
-609< 0.1%
 
-551< 0.1%
 
-54.3761< 0.1%
 
-51.1231< 0.1%
 
-51.081< 0.1%
 
ValueCountFrequency (%) 
3.8551< 0.1%
 
3.7441< 0.1%
 
2.7991< 0.1%
 
1.9631< 0.1%
 
1.831< 0.1%
 

mode
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
120390 
0
49519 
ValueCountFrequency (%) 
112039070.9%
 
04951929.1%
 
2020-12-04T11:00:12.216166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct132940
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Summertime
 
62
Overture
 
43
Home
 
40
Stay
 
34
You
 
33
Other values (132935)
169697 
ValueCountFrequency (%) 
Summertime62< 0.1%
 
Overture43< 0.1%
 
Home40< 0.1%
 
Stay34< 0.1%
 
You33< 0.1%
 
Forever32< 0.1%
 
I Love You32< 0.1%
 
Heaven31< 0.1%
 
Paradise31< 0.1%
 
Angel31< 0.1%
 
Other values (132930)16954099.8%
 
2020-12-04T11:00:13.178162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique113437 ?
Unique (%)66.8%
2020-12-04T11:00:13.480197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length255
Median length18
Mean length23.59821434
Min length1

popularity
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.55660971
Minimum0
Maximum100
Zeros27357
Zeros (%)16.1%
Memory size1.3 MiB
2020-12-04T11:00:13.703113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median33
Q348
95-th percentile65
Maximum100
Range100
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.58261381
Coefficient of variation (CV)0.6839332236
Kurtosis-1.015000807
Mean31.55660971
Median Absolute Deviation (MAD)17
Skewness-0.02152701272
Sum5361752
Variance465.8092188
MonotocityNot monotonic
2020-12-04T11:00:14.004114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02735716.1%
 
4232801.9%
 
4331201.8%
 
4030611.8%
 
4430541.8%
 
4130161.8%
 
4529451.7%
 
3829001.7%
 
3928681.7%
 
3528581.7%
 
Other values (90)11545067.9%
 
ValueCountFrequency (%) 
02735716.1%
 
122541.3%
 
214490.9%
 
312000.7%
 
410740.6%
 
ValueCountFrequency (%) 
1001< 0.1%
 
991< 0.1%
 
971< 0.1%
 
961< 0.1%
 
954< 0.1%
 

release_date
Categorical

HIGH CARDINALITY

Distinct10882
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1945
 
1449
1949
 
1254
1935
 
1123
1948
 
1052
1930-01-01
 
1047
Other values (10877)
163984 
ValueCountFrequency (%) 
194514490.9%
 
194912540.7%
 
193511230.7%
 
194810520.6%
 
1930-01-0110470.6%
 
1940-01-0110080.6%
 
19519860.6%
 
19569660.6%
 
1950-01-019350.6%
 
19579070.5%
 
Other values (10872)15918293.7%
 
2020-12-04T11:00:14.338111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2828 ?
Unique (%)1.7%
2020-12-04T11:00:14.559639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length8.191743816
Min length4

speechiness
Real number (ℝ≥0)

Distinct1628
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09405769441
Minimum0
Maximum0.969
Zeros148
Zeros (%)0.1%
Memory size1.3 MiB
2020-12-04T11:00:14.739638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0281
Q10.0349
median0.045
Q30.0754
95-th percentile0.331
Maximum0.969
Range0.969
Interquartile range (IQR)0.0405

Descriptive statistics

Standard deviation0.1499373026
Coefficient of variation (CV)1.594099276
Kurtosis19.37495526
Mean0.09405769441
Median Absolute Deviation (MAD)0.0131
Skewness4.235812784
Sum15981.2488
Variance0.02248119471
MonotocityNot monotonic
2020-12-04T11:00:14.954635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.03475730.3%
 
0.03195650.3%
 
0.03345600.3%
 
0.03335580.3%
 
0.03375580.3%
 
0.03525570.3%
 
0.03325540.3%
 
0.0345530.3%
 
0.03365500.3%
 
0.0335500.3%
 
Other values (1618)16433196.7%
 
ValueCountFrequency (%) 
01480.1%
 
0.02221< 0.1%
 
0.02233< 0.1%
 
0.02245< 0.1%
 
0.02255< 0.1%
 
ValueCountFrequency (%) 
0.9691< 0.1%
 
0.9683< 0.1%
 
0.9673< 0.1%
 
0.96617< 0.1%
 
0.96522< 0.1%
 

tempo
Real number (ℝ≥0)

Distinct84548
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9480174
Minimum0
Maximum244.091
Zeros147
Zeros (%)0.1%
Memory size1.3 MiB
2020-12-04T11:00:15.207637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.2108
Q193.516
median114.778
Q3135.712
95-th percentile174.4436
Maximum244.091
Range244.091
Interquartile range (IQR)42.196

Descriptive statistics

Standard deviation30.72693665
Coefficient of variation (CV)0.2627401246
Kurtosis-0.0766634995
Mean116.9480174
Median Absolute Deviation (MAD)21.11
Skewness0.4485670215
Sum19870520.68
Variance944.1446362
MonotocityNot monotonic
2020-12-04T11:00:15.409636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01470.1%
 
12021< 0.1%
 
119.98918< 0.1%
 
119.96917< 0.1%
 
120.00517< 0.1%
 
119.99417< 0.1%
 
120.01217< 0.1%
 
129.99516< 0.1%
 
120.01116< 0.1%
 
119.97315< 0.1%
 
Other values (84538)16960899.8%
 
ValueCountFrequency (%) 
01470.1%
 
30.9461< 0.1%
 
31.9881< 0.1%
 
32.4661< 0.1%
 
32.81< 0.1%
 
ValueCountFrequency (%) 
244.0911< 0.1%
 
243.5071< 0.1%
 
243.3721< 0.1%
 
238.8951< 0.1%
 
236.7991< 0.1%
 

valence
Real number (ℝ≥0)

Distinct1739
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5320951423
Minimum0
Maximum1
Zeros185
Zeros (%)0.1%
Memory size1.3 MiB
2020-12-04T11:00:15.616636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0929
Q10.322
median0.544
Q30.749
95-th percentile0.938
Maximum1
Range1
Interquartile range (IQR)0.427

Descriptive statistics

Standard deviation0.2624076309
Coefficient of variation (CV)0.4931592304
Kurtosis-1.05111691
Mean0.5320951423
Median Absolute Deviation (MAD)0.213
Skewness-0.1240095725
Sum90407.75354
Variance0.06885776477
MonotocityNot monotonic
2020-12-04T11:00:15.894640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9617250.4%
 
0.9625980.4%
 
0.9635270.3%
 
0.9644670.3%
 
0.9654050.2%
 
0.963880.2%
 
0.9663560.2%
 
0.9673110.2%
 
0.9682700.2%
 
0.5592540.1%
 
Other values (1729)16560897.5%
 
ValueCountFrequency (%) 
01850.1%
 
1e-0575< 0.1%
 
6.41e-051< 0.1%
 
0.000491< 0.1%
 
0.0005371< 0.1%
 
ValueCountFrequency (%) 
13< 0.1%
 
0.9991< 0.1%
 
0.9981< 0.1%
 
0.9962< 0.1%
 
0.9951< 0.1%
 

year
Real number (ℝ≥0)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.223231
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-12-04T11:00:16.174637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1934
Q11957
median1978
Q31999
95-th percentile2016
Maximum2020
Range99
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.59316763
Coefficient of variation (CV)0.01294399501
Kurtosis-1.026207681
Mean1977.223231
Median Absolute Deviation (MAD)21
Skewness-0.1327482874
Sum335948022
Variance655.0102294
MonotocityNot monotonic
2020-12-04T11:00:16.445651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
197020001.2%
 
198420001.2%
 
196820001.2%
 
196920001.2%
 
201920001.2%
 
197120001.2%
 
197220001.2%
 
197320001.2%
 
197420001.2%
 
197520001.2%
 
Other values (90)14990988.2%
 
ValueCountFrequency (%) 
19211280.1%
 
192272< 0.1%
 
19231690.1%
 
19242370.1%
 
19252630.2%
 
ValueCountFrequency (%) 
202017561.0%
 
201920001.2%
 
201820001.2%
 
201720001.2%
 
201619691.2%
 

Interactions

2020-12-04T10:59:10.604293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:10.903270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:11.177286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:11.442371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:11.729670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:11.996194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:12.245191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:12.713193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:12.968776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:13.232374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:13.500343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:13.743350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:14.005402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:14.253402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:14.505371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:14.744993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:15.239962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:15.794187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:16.405988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:17.053193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:17.560197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:18.178195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:18.778216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:19.402198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:20.137004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:20.793563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:21.126563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:21.569560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:22.058568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:22.453565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:22.763591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:23.043564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:23.332569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:23.580585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:23.819564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:24.050591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:24.324117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:24.578708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:24.833708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:25.247711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:25.480711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:25.713713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:25.955711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:26.196712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:26.443707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:26.704747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:26.952709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:27.228251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:27.467248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:27.693250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:27.922250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:28.150244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:28.372271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:28.601890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:28.841430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:29.102499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:29.327549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:29.553548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:29.791547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:30.048550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:30.285454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:30.508453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:30.746525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:30.989533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:31.213515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:31.460709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:31.682708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:31.909708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:32.161706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:32.413710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:32.668708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:32.910711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:33.155710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:33.391707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:33.619857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:33.859586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:34.100613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:34.327584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:34.725614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:34.975600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:35.242629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:35.534600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:35.790611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:36.075617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:36.426318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:36.723629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:37.030272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:37.317272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:37.600282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:37.919276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:38.256276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:38.596277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:38.938272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:39.269274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:39.600273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:39.923272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:40.243273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:40.515270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:40.881273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:41.200272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:41.525301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:41.832301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:42.193271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:42.573272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:42.844798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:43.110800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:43.414795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:43.664793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:43.926797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:44.170796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:44.410796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:44.656801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:44.905837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:45.145836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:45.411842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:45.664847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:45.900846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:46.167853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:46.423837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:46.676857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:46.950850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:47.281854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:47.873847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:48.126850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:48.400877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:48.665848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:49.150853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:49.405854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:49.673847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:49.961872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:50.234882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:50.563888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:50.843889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:51.136889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:51.415869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:51.747819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:51.990346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:52.300016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:52.595024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:52.876081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:53.209215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:53.431227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:53.762206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:54.075204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:54.431211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:54.769203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:55.130203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:55.474211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:55.746201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:55.990204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:56.295204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:56.618202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:56.922204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:57.182203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:57.432230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:57.709725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:58.003268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:58.273260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:58.675303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:58.974525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:59.368515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T10:59:59.647542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:00.006516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:00.395515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:00.723512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:01.053511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:01.363517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:01.618530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:01.853541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-04T11:00:16.732652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-04T11:00:17.411638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-04T11:00:18.060647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-04T11:00:18.590899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-04T11:00:02.677535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-04T11:00:03.933418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
00.995['Carl Woitschach']0.7081586480.195006KbQ3uYMLKb5jDxLF7wYDD0.563100.1510-12.4281Singende Bataillone 1. Teil019280.0506118.4690.77901928
10.994['Robert Schumann', 'Vladimir Horowitz']0.3792821330.013506KuQTIu1KoTTkLXKrwlLPV0.90180.0763-28.4541Fantasiestücke, Op. 111: Più tosto lento019280.046283.9720.07671928
20.604['Seweryn Goszczyński']0.7491043000.220006L63VW0PibdM1HDSBoqnoM0.00050.1190-19.9240Chapter 1.18 - Zamek kaniowski019280.9290107.1770.88001928
30.995['Francisco Canaro']0.7811807600.130006M94FkXd15sOAOQYRnWPN80.88710.1110-14.7340Bebamos Juntos - Instrumental (Remasterizado)01928-09-250.0926108.0030.72001928
40.990['Frédéric Chopin', 'Vladimir Horowitz']0.2106877330.204006N6tiFZ9vLTSOIxkj8qKrd0.908110.0980-16.8291Polonaise-Fantaisie in A-Flat Major, Op. 61119280.042462.1490.06931928
50.995['Felix Mendelssohn', 'Vladimir Horowitz']0.4243526000.120006NxAf7M8DNHOBTmEd3JSO50.91160.0915-19.2420Scherzo a capriccio: Presto019280.059363.5210.26601928
60.956['Franz Liszt', 'Vladimir Horowitz']0.4441366270.197006O0puPuyrxPjDTHDUgsWI70.435110.0744-17.2261Valse oubliée No. 1 in F-Sharp Major, S. 215/1019280.040080.4950.30501928
70.988['Carl Woitschach']0.5551539670.421006OJjveoYwJdIt76y0Pxpxw0.83610.1050-9.8781Per aspera ad astra019280.0474123.3100.85701928
80.995['Francisco Canaro', 'Charlo']0.6831624930.207006OaJ8Bh7lsBeYoBmwmo2nh0.20690.3370-9.8010Moneda Corriente - Remasterizado01928-10-030.1270119.8330.49301928
90.846['Seweryn Goszczyński']0.6741116000.205006PrZexNb16cabXR8Q418Xc0.00090.1700-20.1191Chapter 1.3 - Zamek kaniowski019280.954081.2490.75901928

Last rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
1698990.3710['YoungBoy Never Broke Again']0.6231610190.721173C80fhriFzangrzWVO4Zp0.000000100.1090-4.5840Rough Ryder642020-04-240.3390166.6370.7192020
1699000.0452['Kelly Clarkson']0.6552161070.71900o58NWBiVXewJNfNDKQyjw0.00001820.1090-7.4001I Dare You692020-04-160.0368124.0340.4352020
1699010.2640['Meek Mill', 'Roddy Ricch']0.7441678450.70210j2CNrgtalXRGIvHMO2vzh0.00000070.1200-6.2550Letter To Nipsey (feat. Roddy Ricch)662020-01-270.288091.8850.3382020
1699020.0227['Trey Songz', 'Summer Walker']0.6191945760.71915QZ11AHm7xiytOGXGlxQi50.00000000.0839-4.1111Back Home (feat. Summer Walker)692020-04-290.157086.0360.3512020
1699030.2100['LEGADO 7', 'Junior H']0.7952185010.585052Cpyvd2dKb6XRn313nH870.00000180.1120-4.4511Ojos De Maniaco682020-02-280.037497.4790.9342020
1699040.1730['DripReport', 'Tyga']0.8751638000.44314KppkflX7I3vJQk7urOJaS0.00003210.0891-7.4611Skechers (feat. Tyga) - Remix752020-05-150.1430100.0120.3062020
1699050.0167['Leon Bridges', 'Terrace Martin']0.7191674680.38501ehhGlTvjtHo2e4xJFB0SZ0.03130080.1110-10.9071Sweeter (feat. Terrace Martin)642020-06-080.0403128.0000.2702020
1699060.5380['Kygo', 'Oh Wonder']0.5141807000.539052eycxprLhK3lPcRLbQiVk0.00233070.1080-9.3321How Would I Know702020-05-290.1050123.7000.1532020
1699070.0714['Cash Cash', 'Andy Grammer']0.6461673080.76103wYOGJYD31sLRmBgCvWxa40.00000010.2220-2.5571I Found You702020-02-280.0385129.9160.4722020
1699080.1090['Ingrid Andress']0.5122147870.428060RFlt48hm0l4Fu0JoccOl0.00000000.1050-7.3871More Hearts Than Mine652020-03-270.027180.5880.3662020